Study on the Yield Prediction Model of Processing Tomato Based on the Grey System Theory

[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theory,and GM（1,1）grey model of processing tomato yield prediction was established.The processing tomato yield in Xinjiang during 2001-2009 was as the example to carry out the instance analysis.[Result] The model had the high forecast accuracy and strong generalization ability,and was reliable for the prediction of recent processing tomato yield.[Conclusion] The research provided the reference for the macro-control of tomato industry,the processing and storage of tomato in Xinjiang.

Prediction model of interval grey number based on DGM(1,1)

In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.

Equivalency and unbiasedness of grey prediction models

In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

Prediction of leaching rate in heap leaching process by grey dynamic model GDM(1,1)

The method of developing GM（1,1） model is extended on the basis of grey system theory. Conditions for the transfer function that improve smoothness of original data sequence and decrease the revert error are given. The grey dynamic model is first combined with the transfer function to predict the leaching rate in heap leaching process. The results show that high prediction accuracy can be expected by using the proposed method. This provides a new approach to realize prediction and control of the future behavior of leaching kinetics.

Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application

Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.

Prediction of syphilis incident rate and number in China based on the GM(1,1)grey model

Objective:To explore the feasibility of using grey model GM(1,1)model to predict syphilis,and to provide a theoretical basis for the health sector to develop corresponding strategies.Methods:GM(1,1)model was used to construct and simulate the incident rate and case number of syphilis in China from 2009 to 2018 to predict the change trend.Results:The GM(1,1)prediction model of syphilis incident rate was x^(1)(k+1)=929.367901 e(0.029413k)-906.297901.The GM(1,1)prediction model for the number of syphilis patients was x^(1)(k+1)=1060.278025 e(0.034280k)-1029.639925.For syphilis incidence model,the posterior difference ratio was 0.19819 and the probability of small error was 1.For the syphilis incident number model,the posterior difference ratio was 0.18450 and the probability of small error was 1.The above models have good fitting accuracy with excellent grade level and can be predicted by extrapolation and predicted that the syphilis incidence in 2019-2021 may be 36.15 per 100,000,37.23 per 100,000 and 38.34 per 100,000,respectively.From 2019 to 2021,the number of incident syphilis cases in China may be 503,406,520,962 and 539,130,respectively.Conclusion:The GM(1,1)model can well fit and predict the change trend of syphilis incidence in time series.The prediction model showed that the incidence of syphilis may continue to increase and the number of syphilis cases per year may continue to increase substantially.More effort is needed to strengthen the prevention and treatment of venereal disease,reduce venereal harm to the population and improve the early detection rate of syphilis.

Prediction Model of Sewing Technical Condition by Grey Neural Network

The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics’ mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.

Prediction of Total Output Value of Construction Industry in Jiangxi Province Based on Grey Prediction Model

In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.

Research on Fault Prediction of Modern Aviation Electronic Equipment Based on Improved Grey Model

The basic principle and method of Grey Model prediction are presented. In view of the defects of general GM(1,1) model, an improved method is proposed. That is using the particle swarm optimization algorithm to obtain the best forecast dimension and using metabolism to make the model parameters adaptively change. Finally, the improved Grey Model is used to predict the fault of high voltage power supply circuit of a certain type of modern air-borne radar. The results which are computed and simulated by Matlab software show that the forecast precision of improved Grey Model is higher than that of original Grey Model.

Analysis of radar fault prediction based on combined model

Based on modeling principle of GM（1,1）model and linear regression model,a combined prediction model is established to predict equipment fault by the fitting of two models.The new prediction model takes full advantage of prediction information provided by the two models and improves the prediction precision.Finally,this model is introduced to predict the system fault time according to the output voltages of a certain type of radar transmitter.

Network traffic prediction by a wavelet-based combined model

Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet-grey-chaos （WGC）, for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM（1,1） and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.

Grey Smoothing Model for Predicting Mine Gas Emission

A grey smoothing model for predicting mine gas emission was presented by combining the grey system theory with the smoothing prediction technique. First of all, according to the variable sequence, GM(1,1) model was set up to predict the general development trend of variable as first fitted values, then the smoothing prediction technique was used to revise the fitted values so as to improve the accuracy of prediction. The results of application in the No.6 Coal Mine in Pingdingshan mining area show that the grey smoothing model has higher accuracy than that of GM(1,1) in predicting the variable sequence with strong fluctuation. The research provides a new scientific method for predicting mine gas emission.

Grey series time-delay predicting model in state estimation for power distribution networks

A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.

Grey Prediction Fuzzy Control of the Target Tracking System in a Robot Weapon

Grey modeling can be used to predict the behavioral development of a system and find out the lead control values of the system. By using fuzzy inference, PID parameters can be adjusted on line by the fuzzy controller with PID parameters self-tuning. According to the characteristics of target tracking system in a robot weapon, grey prediction theory and fuzzy PID control ideas are combined. A grey prediction mathematical model is constructed and a fuzzy PID controller with grey prediction was developed. Simulation result shows fuzzy PID control algorithm with grey prediction is an efficient method that can improve the control equality and robustness of traditional PID control and fuzzy PID control, and has much better performance for target tracking.

The softening prediction of HSLA steel heat-affected zone based on the grey system

The high-strength low-alloy（ HSLA ） steel heat-affected zone （HAZ）softening was predicted using a grey model. HSLA steel DILLIMAX690E, NK-HITEN61OU2 and BHW35 were taken as examples in the research on ultra-narrow gap automatic welding technology. Test results turned out to be that the errors between the values calculated by the Grey Model （GM） （ 1,1 ） model and their actual value were less than 2%, indicating that the grey prediction method could accurately reflect the actual situation of the high-strength low-alloy steel heat-affected zone softening. This method will play a crucial role in guiding the applications of HSLA steel welded structures in the future.

Grey forewarning and prediction for mine water inflowing catastrophe periods

Based on the theory of grey system, established GM （1, 1） grey catastrophe predict model for the first time in order to forecast the catastrophe periods of mine water inflowing （not the volume of water inflowing）. After establishing the grey predict system of the catastrophe regularity of 10 month-average volume of water inflowing, the grey forewarning for mine water inflowing catastrophe periods was established which was used to analyze water disaster in 400 meter level of Wennan Colliery. Based on residual analysis, it shows that the result of grey predict system is almost close to the actual value. And the scene actual result also shows the reliability of prediction. Both the theoretical analysis and the scene actual result indicate feasibility and reliability of the method of grey catastrophe predict system.

A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults.